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Executive Summary

Two data-centric disciplines—Test Data Management (TDM) and AI/ML training data generation—have historically operated in parallel, each serving distinct purposes with separate tools, strategies, and success metrics. TDM has been focused on accelerating test coverage, ensuring compliance, and improving software quality across DevOps pipelines. AI/ML data provisioning, on the other hand, has centered around statistical distributions, large-scale data generation, and eliminating bias in training sets to support intelligent systems. While the business goals and implementation details differ, both arenas share a deep reliance on one critical resource: high-quality, secure, context-aware data. As AI becomes embedded into enterprise software applications, QA processes, and CI/CD workflows, these once-separate domains are converging—both in practice and in purpose. This white paper explores the dynamics of that convergence and argues that GenRocket’s Design-Driven Data platform is the only synthetic data solution uniquely qualified to address both markets simultaneously. At the center of this convergence is the shared requirement for data that is not only realistic and compliant but also intentional and fit for purpose. In software testing, quality data means fewer defects and more reliable deployments. In AI/ML, it means higher prediction accuracy, stronger generalization to unseen data, and reduced bias or unintended behavior in models.
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Overview

A leading health and life insurance provider in the United Kingdom faced mounting challenges in managing test data as it modernized its IT systems. The company had recently transitioned to a microservices-based architecture to enable faster innovation and greater scalability. However, this modernization effort introduced new complexities into their software testing practices, particularly around Test Data Management.
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As data privacy regulations tighten and the risk of data exposure grows, enterprises are rethinking their approach to test data management (TDM). In particular, software engineering and quality assurance (QA) teams are facing new pressure from Chief Information Security Officers (CISOs) and compliance officers to stop using masked production data in lower environments. Increasingly, organizations are turning to synthetic data as a secure and scalable alternative. At GenRocket, we’re seeing this shift accelerate across enterprise quality engineering teams with both new and existing customers.

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Executive Summary

Enterprise software engineering and quality assurance face an ongoing challenge: the lack of reliable, scalable, and compliant test data. Recent industry reports highlight that most enterprises struggle with insufficient, inconsistent, or privacy-compromised test data, hampering the effectiveness of software testing, automation, and overall quality engineering efforts.
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In the realm of software quality assurance and engineering, there exists a wide array of testing categories, each tailored to ensure different aspects of system functionality, performance, and compliance. These range from unit testing, which examines individual components, to complex integrations, performance benchmarking, and regulatory compliance checks. For each testing category, having precise and relevant test data is crucial to accurately assess system behavior and ensure quality.

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Introduction

In the rapidly evolving landscape of insurance technology, Guidewire stands as a cornerstone solution, offering a comprehensive suite for managing policies, claims, and billing. As insurance companies increasingly adopt and customize Guidewire’s platform, the need for efficient, secure, and comprehensive test data automation solutions becomes paramount. GenRocket presents an advanced test data solution to address the unique challenges faced by Guidewire users in generating, automating, and securing test data throughout the software development lifecycle.
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